Shapley additive explanations (SHAP) [1]} takes a similar but distinct approach from LIME by approximating the Shapley values of the conditional expectation of a model.
Feature selection is controlled using a regularization term.
Note that when we write SHAP, we are specifically referring to Kernel SHAP, which is distinguished from its other variants for trees,
structured data, etc.
An extension of SHAP to handle dependent features has also been proposed [2]}, which we refer to as SHAPR after the associated R package. In effort to improve the accuracy of SHAP explanations, SHAPR estimates the conditional distribution assuming features are statistically dependent. Several approximations of this distribution are proposed, including the multivariate Gaussian and empirical conditional distributions.
